WO2021139146A1

WO2021139146A1 - Information recommendation method, device, computer-readable storage medium, and apparatus

Info

Publication number: WO2021139146A1
Application number: PCT/CN2020/106345
Authority: WO
Inventors: 邹洪伟
Original assignee: 平安国际智慧城市科技股份有限公司
Priority date: 2020-01-09
Filing date: 2020-07-31
Publication date: 2021-07-15
Also published as: CN111241265A

Abstract

Disclosed by the present application are an information recommendation method, device, storage medium, and apparatus; the method is: obtaining to-be-recommended personal information and pre-diagnosis information of a user; finding patient medical data corresponding to personal information from a hospital information system; classifying the medical data of patients according to department of the medical consultation to obtain the historical department of the medical consultation of a user to be recommended and corresponding treatment keywords of each historical department of medical consultation; matching all the words obtained by word segmentation processing on pre-diagnosis information with the corresponding medical keywords of each historical department of medical consultation; recommending the matched historical department of the medical consultation.

Description

Information recommendation method, equipment, computer readable storage medium and device

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office on January 9, 2020, the application number is 202010024389.9, and the invention title is "information recommendation methods, equipment, storage media and devices", the entire contents of which are incorporated by reference In this application.

Technical field

This application relates to the technical field of artificial intelligence, and in particular to an information recommendation method, equipment, computer-readable storage medium and device.

Background technique

At this stage, hospitals mainly have hospital information systems (Hospital Information System, HIS), Laboratory Information Management System (Laboratory Information Management System, LIS), medical image archiving and communication system (Picture archiving and communication systems (PACS), radiology information management system (Radioiogy information system, RIS) and electronic medical records (Electronic Medical Record, EMR) and other information systems, the inventor found that these systems generally use patient medical record cards for authentication operations. Whenever you go to a place, you must first swipe your card to obtain patient-related information, and there are too many repeated operations. At present, hospital registration requires multi-level selection according to the default directory of the registration system to complete the registration operation. The registration process is cumbersome and time-consuming.

The above content is only used to assist the understanding of the technical solution of the application, and does not mean that the above content is recognized as prior art.

Technical solutions

The main purpose of this application is to provide an information recommendation method, equipment, storage medium, and device, aiming to solve the technical problem of low registration efficiency in traditional Chinese hospitals in the prior art.

In order to achieve the above objective, this application provides an information recommendation method, the information recommendation method includes the following operations:

Acquiring the personal information of the user to be recommended, and acquiring the pre-examination information of the user to be recommended;

Find the patient medical data corresponding to the personal information from the hospital information system;

Classify the patient's medical data according to the medical department, and obtain the historical medical department of the user to be recommended and the medical keywords corresponding to each historical medical department;

Perform word segmentation processing on the pre-interrogation information, and respectively match all words obtained by performing word segmentation processing on the pre-interrogation information with the medical treatment keywords corresponding to each historical medical treatment department;

After it is determined that the matching is successful, the historical consultation department with the successful matching is taken as the first target department, and the first target department is recommended.

In addition, in order to achieve the above-mentioned object, this application also proposes an information recommendation device. The information recommendation device includes a memory, a processor, and an information recommendation program stored on the memory and running on the processor. The information recommendation program is configured to implement the following operations:

In addition, in order to achieve the above-mentioned object, this application also proposes a computer-readable storage medium on which an information recommendation program is stored, and when the information recommendation program is executed by a processor, the following operations are implemented:

In addition, in order to achieve the above objective, this application also proposes an information recommendation device, the information recommendation device including:

The obtaining module is configured to obtain the personal information of the user to be recommended, and to obtain the pre-examination information of the user to be recommended;

The search module is configured to search for patient medical data corresponding to the personal information from the hospital information system;

The classification module is configured to classify the medical data of the patient according to the medical department, and obtain the historical medical department of the user to be recommended and the medical keywords corresponding to each historical medical department;

The matching module is configured to perform word segmentation processing on the pre-questioning information, and matching all words obtained by performing word segmentation processing on the pre-questioning information with the medical keywords corresponding to each historical medical treatment department;

The recommendation module is configured to, after determining that the matching is successful, use the matched historical consultation department as the first target department, and recommend the first target department.

In this application, the patient medical data corresponding to the personal information of the user to be recommended is searched from the hospital information system, and the pre-examination information is obtained. By matching the pre-examination information with the patient’s medical data, the current patient’s physical condition information is combined The patient medical data of the user's history visits can more accurately recommend the user's medical department, without the user having to select the multi-level options of the default directory in the registration system, directly and conveniently recommend the target department to the user, improve the efficiency of registration, and enhance the user experience .

Description of the drawings

FIG. 1 is a schematic structural diagram of an information recommendation device for a hardware operating environment involved in a solution of an embodiment of the present application;

2 is a schematic flowchart of the first embodiment of the application information recommendation method;

FIG. 3 is a schematic flowchart of a second embodiment of an application information recommendation method;

4 is a schematic flowchart of a third embodiment of an application information recommendation method;

FIG. 5 is a structural block diagram of the first embodiment of the information recommendation device of this application.

The realization, functional characteristics, and advantages of the purpose of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings.

Implementation of this application

It should be understood that the specific embodiments described here are only used to explain the present application, and are not used to limit the present application.

Referring to FIG. 1, FIG. 1 is a schematic diagram of the structure of an information recommendation device for a hardware operating environment involved in a solution of an embodiment of the application.

As shown in FIG. 1, the information recommendation device may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), communication bus 1002, user interface 1003, network interface 1004, and memory 1005. Among them, the communication bus 1002 is used to implement connection and communication between these components. The user interface 1003 may include a display screen (Display), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The wired interface of the user interface 1003 may be a USB interface in this application. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WI-FIdelity, WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) memory, or a stable memory (Non-volatile Memory, NVM), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

Those skilled in the art can understand that the structure shown in FIG. 1 does not constitute a limitation on the information recommendation device, and may include more or less components than those shown in the figure, or combine certain components, or different component arrangements.

As shown in FIG. 1, the memory 1005, which is a computer-readable storage medium, may include an operating system, a network communication module, a user interface module, and an information recommendation program.

In the information recommendation device shown in FIG. 1, the network interface 1004 is mainly used to connect to a back-end server and perform data communication with the back-end server; the user interface 1003 is mainly used to connect to user equipment; the information recommendation device is called by the processor 1001 The information recommendation program is stored in the memory 1005 and executes the information recommendation method provided in the embodiment of the present application.

Based on the above hardware structure, an embodiment of the information recommendation method of this application is proposed.

Referring to Fig. 2, Fig. 2 is a schematic flowchart of the first embodiment of the information recommendation method of this application, and the first embodiment of the information recommendation method of this application is proposed.

In the first embodiment, the information recommendation method includes the following operations:

Operation S10: obtain personal information of the user to be recommended, and obtain pre-examination information of the user to be recommended.

It should be understood that the execution subject of this embodiment is the information recommendation device, where the information recommendation device may be an electronic device such as a smart phone, a personal computer, or a server, which is not limited in this embodiment. The personal information includes basic information such as name, age, ID number and gender. The user to be recommended inputs the pre-inquiry information through a terminal. The terminal may be a mobile phone of the user to be recommended, a hospital registration device, or the information recommendation device. The current physical condition information or the information of the department that the user to be recommended enters through the information recommendation device may be selected through the options displayed on the display interface of the information recommendation device to generate the pre-interrogation information, and It may be that the user to be recommended speaks his current physical condition information by voice, specifically including: whether he has a fever, cough, runny nose, back pain, headache or eye pain, etc., and wants to go to medicine, dermatology, or ophthalmology, etc. Intentional department information.

Operation S20: Search for patient medical data corresponding to the personal information from the hospital information system.

It is understandable that for patients who have been treated in a hospital, the hospital information system will record the information of each patient's visit. The patient medical data is the record information of the user to be recommended for historical medical treatment, including information such as historical medical departments, prescribed prescriptions, and basic medical conditions. The personal information of each user and the corresponding patient medical data are recorded in the hospital information system, and the patient medical data corresponding to the personal information can be searched from the hospital information system.

Operation S30: classify the patient's medical data according to the medical department, and obtain the historical medical department of the to-be-recommended user and the medical keywords corresponding to each historical medical department.

It should be noted that usually the same patient goes to the hospital for medical treatment, and may see the same disease, relapsed or not cured, and then comes to the hospital for follow-up visits, then the patient’s medical data can be classified according to the visiting department. Including dermatology, internal medicine, surgery, etc., and extract keywords such as information that can be treated by each historical clinic, and obtain the corresponding treatment information of each historical clinic to perform word segmentation processing, and obtain all words of the treatment disease information , Calculate the term frequency-inverse document frequency (Term Frequency-Inverse Document Frequency, TF-TDF) value. The larger the TF-TDF value, the more important the word. All words can be sorted according to the TF-TDF value from largest to smallest, and the preset number ranked first Words are used as keywords for medical consultation. The preset number can be set according to experience, for example, 3.

Operation S40: performing word segmentation processing on the pre-questioning information, and matching all words obtained by performing word segmentation processing on the pre-questioning information with the medical treatment keywords corresponding to each historical medical department.

It should be understood that the pre-inquiry information includes information such as the current physical condition of the user to be recommended or the clinic to be linked to. The word segmentation processing on the pre-inquiry information can be performed through a dictionary-based word segmentation algorithm or It is string matching. The string to be matched is matched with a sufficiently large dictionary based on a certain algorithm strategy. If the match hits, the word can be segmented. According to different matching strategies, it is divided into forward maximum matching method, reverse maximum matching method, two-way matching word segmentation, full segmentation path selection, etc., so as to obtain all the words of the pre-question information.

In a specific implementation, all words of the pre-interrogation information and the medical treatment keywords corresponding to each historical medical department are expressed in vector form, and all the words of the pre-interrogation information in the vector form are calculated to correspond to each historical medical department. The cosine distance between the treatment keywords is used as the similarity. According to the similarity, it is judged whether all the words of the pre-questioning information match the treatment keywords corresponding to each historical treatment department. The first similarity threshold can be preset by setting For example, 80%, when the similarity exceeds the preset first similarity threshold, the historical consultation department corresponding to the similarity that exceeds the preset first similarity threshold is identified as a department that has been successfully matched. It can also be recommended by selecting the department with the highest similarity in history as the department with a successful match.

Operation S50: After it is determined that the matching is successful, use the historically matched department as the first target department, and recommend the first target department.

It should be noted that the matching historical department is the department for which the user to be recommended needs to register, and then it is used as the first target department for recommendation, which may be the first target department in the information recommendation The display interface of the device is displayed, and the to-be-recommended user confirms the first target department to complete the registration operation. The first target department may also be played by voice, or the first target department may be recommended to the user terminal of the user to be recommended, such as a smart phone or smart watch, and the user to be recommended may be The first target department conducts follow-up registration operations.

In this embodiment, the patient medical data corresponding to the personal information of the user to be recommended is searched from the hospital information system, and the pre-question information is obtained. By matching the pre-question information with the patient’s medical data, the current patient’s physical condition is obtained. The information is combined with the medical data of the user’s medical history to recommend the user’s medical department more accurately, without requiring the user to select the multi-level options in the default directory in the registration system, directly and conveniently recommend the target department to the user, improving the efficiency of registration and improving user experience.

Referring to FIG. 3, FIG. 3 is a schematic flowchart of a second embodiment of the information recommendation method of this application. Based on the first embodiment shown in FIG. 2 above, a second embodiment of the information recommendation method of this application is proposed.

In the second embodiment, after the operation S40, the method further includes:

Operation S401: After determining that the matching fails, obtain other departments and corresponding other department keywords from the hospital information system except for the historical consultation department.

It should be understood that if all the words of the pre-interrogation information fail to match the medical keywords corresponding to each historical medical department, it means that the user to be recommended is not for follow-up visits and needs to add a new department. In the system, the basic information of other departments except the historical department is obtained, the basic information of the other departments is segmented, all the words of the basic information of the other departments are obtained, and the basic information of each word is calculated. TF-TDF value. The larger the TF-TDF value, the more important the word is. All words can be sorted according to the TF-TDF value from large to small, and the preset number of words ranked in the front can be obtained as the treatment Keywords, the preset number can be set according to experience, for example, 3, so as to obtain other department keywords corresponding to each other department.

Operation S402: match all words of the pre-questioning information with keywords of other departments corresponding to other departments.

It is understandable that all the words of the pre-interrogation information and keywords of other departments are expressed in vector form, and the cosine distance between all the words of the pre-question information in the vector form and the keywords of other departments is calculated as similarity According to the similarity, it is judged whether all the words in the pre-question information match the keywords of other departments. A preset second similarity threshold can be set, such as 80%, when the similarity exceeds the preset When the second similarity threshold is used, other departments corresponding to the similarity that exceeds the preset second similarity threshold are identified as departments that have successfully matched. It can also be recommended by selecting other departments with the highest similarity as the departments with a successful match.

Operation S403: after it is determined that the matching is successful, another department that has been successfully matched is used as the second target department, and the second target department is recommended.

It should be noted that the other departments that are successfully matched are the departments that the user to be recommended need to register, and they are recommended as the second target department. The second target department may be listed in the information recommendation device. The display interface of the to-be-recommended user confirms the second target department, and the registration operation can be completed. The second target department may also be played by voice, or the second target department may be recommended to the user terminal of the user to be recommended, such as a smart phone or smart watch. The second target department performs follow-up registration operations.

Operation S404: After determining that the matching fails, send the pre-interrogation information to the target terminal, so that the medical staff can recommend a consultation department based on the pre-interrogation information through the target terminal.

In a specific implementation, there may be cases where the pre-inquiry information is not accurately entered, resulting in failure to match a suitable department for recommendation, then the pre-inquiry information can be sent to the target terminal, which may be The personal computer or smart phone of the medical staff, so that the medical staff can view the pre-inquiry information through the target terminal, and recommend the medical department for the user to be recommended according to the pre-inquiry information, so as to increase the number of users to be recommended The efficiency of registration.

In this embodiment, when a suitable department cannot be found from the historical consultation department for recommendation, by obtaining other departments except the historical consultation department and the corresponding other department keywords, according to the pre-question information and The keywords of the other departments are to recommend suitable departments for the users to be recommended, which improves the registration efficiency of the users to be recommended.

Referring to Fig. 4, Fig. 4 is a schematic flowchart of a third embodiment of the information recommendation method of this application. Based on the above-mentioned first or second embodiment, a third embodiment of the information recommendation method of this application is proposed. This embodiment is described based on the first embodiment.

In the third embodiment, after the operation S10, the method further includes:

Operation S101: Obtain a current photo of the user to be recommended.

It should be understood that the user to be recommended is usually a patient who goes to the hospital to see a doctor. High-definition cameras are installed at the entrance of the building, the registration office or the entrance of each clinic, and the facial information and expression information of the patient are captured in real time. When the user enters the hospital, the current photo of the user to be recommended is taken through a camera or other shooting equipment.

Operation S102: Perform feature extraction on the current photo to obtain the feature of the picture to be recognized corresponding to the current photo.

It should be noted that the current photo may be pre-processed, specifically, face detection and registration, face cutting, and image normalization. A deep learning target detection algorithm based on candidate regions (Fast Region-based Convolutional Neural Networks, Fast-RCNN) to detect human faces, perform face cutting on the current photo, and obtain the face picture of the user to be recommended. Image normalization is performed on the face picture, and the purpose of geometric normalization is mainly to transform the expression sub-images into a uniform size, which is beneficial to the extraction of expression features.

Further, the operation S102 includes:

Geometrically normalize the current photo to obtain a geometrically normalized picture;

Performing gray-scale normalization on the geometrically normalized picture to obtain a gray-scale normalized picture;

Perform feature extraction on the gray-scale normalized picture through a principal component analysis algorithm to obtain the feature of the picture to be recognized corresponding to the current photo.

It should be understood that, first of all, to calibrate the feature points of the current photo, here [x,y] = The ginput(3) function is used to calibrate the three feature points of the eyes and nose, mainly by using the mouse to calibrate, and obtain the coordinate values of the three feature points. Then rotate the image according to the coordinate values of the left and right eyes to ensure the consistency of the face orientation. Suppose the distance between the two eyes is d, and the midpoint is O. Determine the rectangular feature area according to the facial feature points and the geometric model, take O as the reference, cut d on the left and right sides, and take the rectangular areas of 0.5d and 1.5d in the vertical direction for cutting. The scale transformation of the expression sub-region image into a uniform size is more conducive to the extraction of expression features. The intercepted image is unified into a 90*100 image, and the geometric normalization of the image is realized, and the geometric normalized picture is obtained.

It is understandable that the gray scale normalization can also be performed on the geometrically normalized picture. The gray scale normalization mainly increases the brightness of the image, makes the details of the image clearer, and reduces the influence of light and light intensity. A preset image function can be used for illumination compensation, and the preset image function can be image=255*imadjust(C/255,[0.3;1],[0;1]) to obtain the grayscale normalized image .

It should be noted that the feature extraction is performed on the gray-scale normalized image through the Principal Component Analysis (PCA) algorithm. The principal component is the linear coefficient, that is, the projection direction, and the center of the coordinate axis is moved to the center of the data. , And then rotate the coordinate axis so that the variance of the data on the C1 axis is the largest, that is, the projections of all n data individuals in this direction are the most scattered, then more information is retained, C1 becomes the first principal component, C2 The second principal component, find a C2, make the covariance (correlation coefficient) of C2 and C1 0, so as not to overlap with C1 information, and maximize the variance of the data in this direction, and so on, find the third principal component, The fourth principal component... the p-th principal component. There are p principal components for p random variables. The feature value and feature vector are analyzed through covariance, and the feature vector is the feature face to obtain the feature of the image to be recognized. Based on dynamic pictures, geometric methods or deep learning methods can also be used.

Operation S103: Perform micro-expression detection according to the feature of the picture to be recognized, and obtain the current expression of the user to be recommended.

It is understandable that micro-expression detection is used to predict the patient's mental state, such as whether the patient is happy, uncomfortable, angry, or sad. Hospital staff will treat different patients with different attitudes according to the patient's psychological state to reduce doctor-patient disputes. For example: When the patient is in an angry state of mind, the medical staff should try to use a gentle tone to avoid irritating the patient and causing medical injuries; when the patient is in a sad state of mind, the medical staff should try not to despise the patient and say more encouragement Words, to prevent the patient from being more sad, or even triggering suicide.

Further, the operation S103 includes:

Learning the features of the picture to be recognized through a recurrent neural network model, and classifying the learned features through a random forest model, to obtain the current expression of the user to be recommended.

It should be understood that micro-expression detection uses deep learning through a multi-layer network structure, such as a recurrent neural network (Recurrent Neuron). Networks, RNNs) perform a variety of non-linear transformations and representations to extract the high-level abstract features of the picture. After learning the deep features, the last step is to identify which type of basic expression the expression of the test face belongs to. Deep neural networks can recognize facial expressions end-to-end. One way is to add a loss layer at the end of the network to correct the back propagation error. The predicted probability of each sample can be directly output from the network. Another way is to use a deep neural network as a tool to extract features, and then use a traditional classifier, such as a random forest model, to classify the extracted features, so as to obtain the current expression of the user to be recommended.

Operation S104: Find the corresponding service attitude suggestion according to the current expression, and send the service attitude suggestion to the target terminal.

It should be noted that the current facial expressions of the user to be recommended are detected, and the current facial expressions include happy, uncomfortable, angry, sad, impatient, and so on. Corresponding service attitude suggestions can be preset for various expressions. For example, when the current expression is angry, the corresponding service attitude suggestions are: use a gentle tone to avoid irritating the patient. The target terminal may be a smart phone or a computer of a medical staff, so that the medical staff can understand the mood of the patient in time and find a suitable way to communicate, so as to improve the efficiency of treatment.

Further, in this embodiment, the obtaining the personal information of the user to be recommended includes:

Obtain a photo collection from the public security system library, perform feature extraction on each photo in the photo collection, and obtain the photo feature corresponding to each photo;

Matching the feature of the picture to be recognized with the feature of each photo, and after determining that the matching is successful, identify the target user corresponding to the successfully matched photo as the user to be recommended;

The personal information of the target user is obtained from the public security system database as the personal information of the user to be recommended.

It should be understood that the photo features corresponding to each photo can be used to align the face to the average face, so that the positions of the face feature points in all images are almost the same after the alignment. Intuitively, the face recognition algorithm trained with aligned images is more effective. Perform facial feature point positioning on the image features to be recognized to obtain the face feature points to be processed corresponding to the image features to be recognized; compare the face feature points to be processed with preset facial feature points to obtain a homography matrix; The homography matrix transforms the face in the photo to obtain a calibrated face picture; compares the calibrated face picture with each photo feature in the security system library through a convolutional neural network model to obtain the to-be-recognized The similarity of the face between the image feature and each photo feature. If the face similarity exceeds the preset face similarity threshold, it is considered that the matching is successful, and the user corresponding to the photo in the public security system library of the successful matching is taken as the target user. The preset face similarity threshold may be set according to an empirical value, such as 80%.

Further, in this embodiment, after the personal information of the target user is obtained from the public security system database as the personal information of the user to be recommended, the information recommendation method further includes:

Judging whether the user to be recommended is a doctor-patient risk object according to the personal information of the user to be recommended, and obtaining a judgment result;

Sending the judgment result to the target terminal.

It is understandable that the personal information of the user to be recommended is obtained through the public security system database through face recognition. If the user to be recommended has a criminal record, it is determined that the user to be recommended is a doctor-patient risk object, and the The judgment result is sent to the target terminal. The target terminal may be a smartphone or computer of a medical staff to notify the security staff and medical staff to pay attention to prevent medical injuries. When the micro-expression detects that the patient is in a pre-injury and medical condition, it will proactively notify the security and public security organs, let them come to protect and stop, and notify the medical staff to take protective measures to ensure their own safety and the safety of hospital property. And it can be compared with the medical trouble database of the medical system, focusing on prevention of medical troubles, security guards and hospital staff, take protective measures, and actively collect relevant evidence to prevent medical troubles from happening. In response to medical incidents, the collected data can be submitted to management agencies and judicial organs to crack down on medical personnel with reasonable evidence.

In this embodiment, by obtaining the current photo of the user to be recommended, performing feature extraction on the current photo, obtaining the feature of the image to be recognized corresponding to the current photo, and performing micro-expression detection based on the feature of the image to be recognized to obtain The current expression of the user to be recommended searches for the corresponding service attitude suggestion based on the current expression, and sends the service attitude suggestion to the target terminal so that the medical staff can understand the patient’s mood in time and find a suitable way to communicate. Improve the efficiency of treatment.

In addition, an embodiment of the present application also proposes a computer-readable storage medium having an information recommendation program stored on the computer-readable storage medium, and when the information recommendation program is executed by a processor, the information recommendation method described above is implemented. operating. The computer-readable storage medium may be non-volatile or volatile, and the computer-readable storage medium may be referred to as "storage medium" for short.

In addition, referring to FIG. 5, an embodiment of the present application also proposes an information recommendation device, and the information recommendation device includes:

The obtaining module 10 is configured to obtain the personal information of the user to be recommended, and to obtain the pre-examination information of the user to be recommended.

It should be understood that the personal information includes basic information such as name, age, ID number and gender. The user to be recommended inputs the pre-inquiry information through a terminal, and the terminal may be a mobile phone of the user to be recommended, a hospital registration device, or the information recommendation device. The current physical condition information or the information of the department that the user to be recommended enters through the information recommendation device may be selected through the options displayed on the display interface of the information recommendation device to generate the pre-interrogation information, and It may be that the user to be recommended speaks his current physical condition information through voice, specifically including: whether he has fever, cough, runny nose, low back pain, headache or eye pain, etc., and wants to go to medicine, dermatology or ophthalmology, etc. Intentional department information.

The searching module 20 is configured to search for patient medical data corresponding to the personal information from the hospital information system.

The classification module 30 is configured to classify the medical data of the patient according to the medical department, and obtain the historical medical department of the user to be recommended and the medical keywords corresponding to each historical medical department.

It should be noted that usually the same patient goes to the hospital for medical treatment, and may see the same disease, relapsed or not cured, and then comes to the hospital for follow-up visits, then the patient’s medical data can be classified according to the visiting department. Including dermatology, internal medicine, surgery, etc., and extract keywords such as information that can be treated by each historical clinic, and obtain the corresponding treatment information of each historical clinic to perform word segmentation processing, and obtain all words of the treatment disease information , Calculate the term frequency-inverse document frequency (Term Frequency-Inverse Document Frequency, TF-TDF) value. The larger the TF-TDF value, the more important the word. All words can be sorted according to the TF-TDF value from largest to smallest, and the preset number ranked first Words are used as keywords for medical consultation. The preset number can be set based on experience, for example, 3.

The matching module 40 is configured to perform word segmentation processing on the pre-questioning information, and matching all words obtained by performing word segmentation processing on the pre-questioning information with the medical treatment keywords corresponding to each historical medical department.

The recommendation module 50 is configured to, after determining that the matching is successful, use the matched historical consultation department as the first target department, and recommend the first target department.

In an embodiment, the information recommendation device further includes:

The obtaining module 10 is further configured to obtain other departments and corresponding keywords of other departments from the hospital information system after determining that the matching fails;

The matching module 40 is further configured to match all words of the pre-questioning information with keywords of other departments corresponding to other departments;

The recommendation module 50 is further configured to, after determining that the matching is successful, use other departments that are successfully matched as the second target department, and recommend the second target department;

The sending module is configured to send the pre-interrogation information to a target terminal after determining that the matching fails, so that the medical staff can recommend a treatment department based on the pre-interrogation information through the target terminal.

In an embodiment, the information recommendation device further includes:

The obtaining module 10 is further configured to obtain the current photo of the user to be recommended;

The feature extraction module is configured to perform feature extraction on the current photo to obtain the feature of the picture to be recognized corresponding to the current photo;

The micro-expression detection module is configured to perform micro-expression detection according to the characteristics of the picture to be recognized, and obtain the current expression of the user to be recommended;

The search module 20 is further configured to search for a corresponding service attitude suggestion according to the current expression, and send the service attitude suggestion to the target terminal.

In an embodiment, the feature extraction module is configured to perform geometric normalization on the current photo to obtain a geometrically normalized picture; perform gray-scale normalization on the geometrically normalized picture to obtain a grayscale A normalized picture; feature extraction is performed on the gray-scale normalized picture through a principal component analysis algorithm to obtain the characteristics of the picture to be recognized corresponding to the current photo.

In one embodiment, the classification module 30 is further configured to learn the features of the image to be recognized through a recurrent neural network model, and to classify the learned features through a random forest model, to obtain the current profile of the user to be recommended expression.

In one embodiment, the acquisition module 10 is further configured to acquire a photo collection from a public security system library, perform feature extraction on each photo in the photo collection, and obtain photo features corresponding to each photo; and compare the features of the pictures to be identified with Each photo feature is matched, and after determining that the matching is successful, the target user corresponding to the successfully matched photo is identified as the user to be recommended; the personal information of the target user is obtained from the public security system database as the personal information of the user to be recommended .

In an embodiment, the information recommendation device further includes:

A judging module, configured to judge whether the user to be recommended is a doctor-patient risk object according to the personal information of the user to be recommended, and obtain a judgment result;

The sending module is further configured to send the judgment result to the target terminal.

For other embodiments or specific implementation manners of the information recommendation apparatus described in this application, reference may be made to the foregoing method embodiments, which will not be repeated here.

It should be noted that in this article, the terms "include", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements not only includes those elements, It also includes other elements that are not explicitly listed, or elements inherent to the process, method, article, or system. If there are no more restrictions, the element defined by the sentence "including a..." does not exclude the existence of other identical elements in the process, method, article, or system that includes the element.

The serial numbers of the foregoing embodiments of the present application are for description only, and do not represent the advantages and disadvantages of the embodiments. In the unit claims listing several devices, several of these devices may be embodied in the same hardware item. The use of the words first, second, and third does not indicate any order, and these words may be interpreted as signs.

Through the description of the above implementation manners, those skilled in the art can clearly understand that the above-mentioned embodiment method can be implemented by means of software plus the necessary general hardware platform, of course, it can also be implemented by hardware, but in many cases the former is better.的实施方式。 Based on this understanding, the technical solution of this application essentially or the part that contributes to the existing technology can be embodied in the form of a software product, and the computer software product is stored in a storage medium (such as a read-only memory mirror (Read Only)). Memory image, ROM)/Random Access Memory (Random Access Memory, RAM, magnetic disk, optical disk), including several instructions to make a terminal device (can be a mobile phone, computer, server, air conditioner, or network equipment, etc.) ) Perform the methods described in each embodiment of the present application.

The above are only the preferred embodiments of the application, and do not limit the scope of the patent for this application. Any equivalent structure or equivalent process transformation made using the content of the description and drawings of the application, or directly or indirectly applied to other related technical fields , The same reason is included in the scope of patent protection of this application.

Claims

An information recommendation method, the information recommendation method includes the following operations:

Acquiring the personal information of the user to be recommended, and acquiring the pre-examination information of the user to be recommended;

Find the patient medical data corresponding to the personal information from the hospital information system;

Classify the patient's medical data according to the medical department, and obtain the historical medical department of the user to be recommended and the medical keywords corresponding to each historical medical department;

Perform word segmentation processing on the pre-interrogation information, and respectively match all words obtained by performing word segmentation processing on the pre-interrogation information with the medical treatment keywords corresponding to each historical medical treatment department;

After it is determined that the matching is successful, the historical consultation department with the successful matching is taken as the first target department, and the first target department is recommended.
The information recommendation method according to claim 1, wherein, in the “word segmentation processing on the pre-questioning information, and all words obtained by performing word segmentation processing on the pre-questioning information, corresponding to each historical medical department After matching the keywords of the doctor’s visit, the information recommendation method further includes:

After determining that the matching fails, obtain other departments and corresponding other department keywords from the hospital information system except for the historical consultation department;

Match all words of the pre-question information with keywords of other departments corresponding to other departments;

After it is determined that the matching is successful, other departments that have been successfully matched are used as the second target departments, and the second target departments are recommended;

After determining that the matching fails, the pre-questioning information is sent to the target terminal, so that the medical staff can recommend a consultation department based on the pre-questioning information through the target terminal.
5. The information recommendation method according to claim 1, wherein after said "acquiring personal information of the user to be recommended and obtaining pre-examination information of the user to be recommended", the information recommendation method further comprises:

Obtaining the current photo of the user to be recommended;

Performing feature extraction on the current photo to obtain the feature of the image to be recognized corresponding to the current photo;

Performing micro-expression detection according to the characteristics of the picture to be recognized, and obtaining the current expression of the user to be recommended;

The corresponding service attitude suggestion is searched for according to the current expression, and the service attitude suggestion is sent to the target terminal.
8. The information recommendation method according to claim 3, wherein said "extracting features of the current photo to obtain the features of the image to be recognized corresponding to the current photo" includes:

Geometrically normalize the current photo to obtain a geometrically normalized picture;

Performing gray-scale normalization on the geometrically normalized picture to obtain a gray-scale normalized picture;

Perform feature extraction on the gray-scale normalized picture through a principal component analysis algorithm to obtain the feature of the picture to be recognized corresponding to the current photo.
8. The information recommendation method according to claim 3, wherein said "detecting micro-expression based on the features of the picture to be recognized to obtain the current expression of the user to be recommended" includes:

Learning the features of the picture to be recognized through a recurrent neural network model, and classifying the learned features through a random forest model, to obtain the current expression of the user to be recommended.
5. The information recommendation method according to any one of claims 1 to 5, wherein the "acquiring personal information of the user to be recommended" includes:

Obtain a photo collection from the public security system library, perform feature extraction on each photo in the photo collection, and obtain the photo feature corresponding to each photo;

Matching the feature of the picture to be recognized with the feature of each photo, and after determining that the matching is successful, identify the target user corresponding to the successfully matched photo as the user to be recommended;

The personal information of the target user is obtained from the public security system database as the personal information of the user to be recommended.
8. The information recommendation method according to claim 6, wherein after the "acquiring the personal information of the target user from the public security system database as the personal information of the user to be recommended", the information recommendation method further comprises:

Judging whether the user to be recommended is a doctor-patient risk object according to the personal information of the user to be recommended, and obtaining a judgment result;

Sending the judgment result to the target terminal.
An information recommendation device, the information recommendation device comprising: a memory, a processor, and an information recommendation program stored on the memory and running on the processor, and when the information recommendation program is executed by the processor To achieve the following operations:

Acquiring the personal information of the user to be recommended, and acquiring the pre-examination information of the user to be recommended;

Find the patient medical data corresponding to the personal information from the hospital information system;

Classify the patient's medical data according to the medical department, and obtain the historical medical department of the user to be recommended and the medical keywords corresponding to each historical medical department;

Perform word segmentation processing on the pre-interrogation information, and respectively match all words obtained by performing word segmentation processing on the pre-interrogation information with the medical treatment keywords corresponding to each historical medical treatment department;

After it is determined that the matching is successful, the historical consultation department with the successful matching is taken as the first target department, and the first target department is recommended.
The information recommendation device according to claim 8, wherein, when the information recommendation program is executed by the processor, word segmentation processing is performed on the pre-question information, and the pre-question information will be After matching all the words obtained by word segmentation with the corresponding medical keywords of each historical medical department", the following operations are also implemented:

After determining that the matching fails, obtain other departments and corresponding other department keywords from the hospital information system except for the historical consultation department;

Match all words of the pre-question information with keywords of other departments corresponding to other departments;

After it is determined that the matching is successful, other departments that have been successfully matched are used as the second target departments, and the second target departments are recommended;

After determining that the matching fails, the pre-questioning information is sent to the target terminal, so that the medical staff can recommend a consultation department based on the pre-questioning information through the target terminal.
8. The information recommendation device according to claim 8, wherein, when the information recommendation program is executed by the processor, in the "acquire personal information of the user to be recommended, and obtain the pre-examination information of the user to be recommended" "After that, the following operations are also implemented:

Obtaining the current photo of the user to be recommended;

Performing feature extraction on the current photo to obtain the feature of the image to be recognized corresponding to the current photo;

Performing micro-expression detection according to the characteristics of the picture to be recognized, and obtaining the current expression of the user to be recommended;

The corresponding service attitude suggestion is searched for according to the current expression, and the service attitude suggestion is sent to the target terminal.
10. The information recommendation device according to claim 10, wherein the "feature extraction on the current photo to obtain the feature of the picture to be recognized corresponding to the current photo" includes:

Geometrically normalize the current photo to obtain a geometrically normalized picture;

Performing gray-scale normalization on the geometrically normalized picture to obtain a gray-scale normalized picture;

Perform feature extraction on the gray-scale normalized picture through a principal component analysis algorithm to obtain the feature of the picture to be recognized corresponding to the current photo.
10. The information recommendation device according to claim 10, wherein said "detecting micro-expression based on the features of the picture to be recognized to obtain the current expression of the user to be recommended" comprises:

Learning the features of the picture to be recognized through a recurrent neural network model, and classifying the learned features through a random forest model, to obtain the current expression of the user to be recommended.
The information recommendation device according to any one of claims 8-12, wherein the "acquiring personal information of the user to be recommended" includes:

Obtain a photo collection from the public security system library, perform feature extraction on each photo in the photo collection, and obtain the photo feature corresponding to each photo;

Matching the feature of the picture to be recognized with the feature of each photo, and after determining that the matching is successful, identify the target user corresponding to the successfully matched photo as the user to be recommended;

The personal information of the target user is obtained from the public security system database as the personal information of the user to be recommended.
The information recommendation device according to claim 13, wherein when the information recommendation program is executed by the processor, the "acquire the personal information of the target user from the public security system database as the personal information of the user to be recommended" After "Information", the following operations are also implemented:

Judging whether the user to be recommended is a doctor-patient risk object according to the personal information of the user to be recommended, and obtaining a judgment result;

Sending the judgment result to the target terminal.
A computer-readable storage medium having an information recommendation program stored on the computer-readable storage medium, and when the information recommendation program is executed by a processor, the following operations are implemented:

Acquiring the personal information of the user to be recommended, and acquiring the pre-examination information of the user to be recommended;

Find the patient medical data corresponding to the personal information from the hospital information system;

Classify the patient's medical data according to the medical department, and obtain the historical medical department of the user to be recommended and the medical keywords corresponding to each historical medical department;

Perform word segmentation processing on the pre-interrogation information, and respectively match all words obtained by performing word segmentation processing on the pre-interrogation information with the medical treatment keywords corresponding to each historical medical treatment department;

After it is determined that the matching is successful, the historical consultation department with the successful matching is taken as the first target department, and the first target department is recommended.
The computer-readable storage medium according to claim 15, wherein, when the information recommendation program is executed by the processor, the word segmentation process is performed on the pre-question information and the pre-question All words obtained by word segmentation of the medical information are matched with the corresponding medical keywords of each historical medical department", the following operations are also implemented:

After determining that the matching fails, obtain other departments and corresponding other department keywords from the hospital information system except for the historical consultation department;

Match all words of the pre-question information with keywords of other departments corresponding to other departments;

After it is determined that the matching is successful, other departments that have been successfully matched are used as the second target departments, and the second target departments are recommended;

After determining that the matching fails, the pre-questioning information is sent to the target terminal, so that the medical staff can recommend a consultation department based on the pre-questioning information through the target terminal.
The computer-readable storage medium according to claim 15, wherein, when the information recommendation program is executed by the processor, the information recommendation program is executed in the "acquire personal information of the user to be recommended, and obtain the pre-question of the user to be recommended" After "Diagnosis Information", the following operations are also implemented:

Obtaining the current photo of the user to be recommended;

Performing feature extraction on the current photo to obtain the feature of the image to be recognized corresponding to the current photo;

Performing micro-expression detection according to the characteristics of the picture to be recognized, and obtaining the current expression of the user to be recommended;

The corresponding service attitude suggestion is searched for according to the current expression, and the service attitude suggestion is sent to the target terminal.
17. The computer-readable storage medium according to claim 17, wherein said "extracting features of the current photo to obtain the features of the image to be recognized corresponding to the current photo" comprises:

Geometrically normalize the current photo to obtain a geometrically normalized picture;

Performing gray-scale normalization on the geometrically normalized picture to obtain a gray-scale normalized picture;

Perform feature extraction on the gray-scale normalized picture through a principal component analysis algorithm to obtain the feature of the picture to be recognized corresponding to the current photo.
17. The computer-readable storage medium of claim 17, wherein the "detecting micro-expression based on the characteristics of the picture to be recognized to obtain the current expression of the user to be recommended" comprises:

Learning the features of the picture to be recognized through a recurrent neural network model, and classifying the learned features through a random forest model, to obtain the current expression of the user to be recommended.
18. The computer-readable storage medium according to any one of claims 15-19, wherein the "acquiring personal information of the user to be recommended" includes:

Obtain a photo collection from the public security system library, perform feature extraction on each photo in the photo collection, and obtain the photo feature corresponding to each photo;

Matching the feature of the picture to be recognized with the feature of each photo, and after determining that the matching is successful, identify the target user corresponding to the successfully matched photo as the user to be recommended;

The personal information of the target user is obtained from the public security system database as the personal information of the user to be recommended.
The computer-readable storage medium according to claim 20, wherein when the information recommendation program is executed by the processor, the personal information of the target user is obtained from the public security system library as the to-be-recommended After the user’s personal information", the following operations are also implemented:

Judging whether the user to be recommended is a doctor-patient risk object according to the personal information of the user to be recommended, and obtaining a judgment result;

Sending the judgment result to the target terminal.
An information recommendation device, the information recommendation device comprising:

The obtaining module is configured to obtain the personal information of the user to be recommended, and to obtain the pre-examination information of the user to be recommended;

The search module is configured to search for patient medical data corresponding to the personal information from the hospital information system;

The classification module is configured to classify the medical data of the patient according to the medical department, and obtain the historical medical department of the user to be recommended and the medical keywords corresponding to each historical medical department;

The matching module is configured to perform word segmentation processing on the pre-questioning information, and matching all words obtained by performing word segmentation processing on the pre-questioning information with the medical keywords corresponding to each historical medical treatment department;

The recommendation module is configured to, after determining that the matching is successful, use the matched historical consultation department as the first target department, and recommend the first target department.